Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease

Henry Cavanagh, Andreas Mosbach, Gabriel Scalliet, Rob Lind and Robert G. Endres ()
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Henry Cavanagh: Imperial College London
Andreas Mosbach: Syngenta Crop Protection AG
Gabriel Scalliet: Syngenta Crop Protection AG
Rob Lind: Syngenta International Research Centre
Robert G. Endres: Imperial College London

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling.

Date: 2021
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DOI: 10.1038/s41467-021-26577-1

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